{"id":"W4306827332","doi":"10.1016/j.jcmg.2022.07.017","title":"Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning","year":2022,"lang":"en","type":"article","venue":"JACC. Cardiovascular imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"National Heart, Lung, and Blood Institute","keywords":"Mace; Medicine; Myocardial perfusion imaging; Myocardial infarction; Internal medicine; Quartile; Cardiology; Area under the curve; Perfusion; Receiver operating characteristic; Logistic regression; Perfusion scanning; Confidence interval; Percutaneous coronary intervention","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002674878,0.0005144664,0.001244891,0.0004292889,0.00188649,0.0002875294,0.0002405292,0.00005093908,0.0002779962],"category_scores_gemma":[0.0007616487,0.0005788219,0.002435387,0.0006327493,0.0001056497,0.0003878317,0.0008595486,0.001498059,0.00002802696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230829,"about_ca_system_score_gemma":0.0003002979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003870356,"about_ca_topic_score_gemma":0.000001784321,"domain_scores_codex":[0.9943609,0.001163707,0.0005477245,0.001139317,0.001839871,0.0009484696],"domain_scores_gemma":[0.9975171,0.0004215912,0.0002223897,0.001251291,0.0002711017,0.0003165165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008582368,0.00013523,0.7491593,0.00004657811,0.001663076,0.002148933,0.0009918243,0.1311277,0.003183053,0.000008639721,0.001012418,0.1104374],"study_design_scores_gemma":[0.00954592,0.0001030952,0.3093823,0.0003058927,0.01130009,0.002329948,0.013507,0.376509,0.001930223,0.0001419544,0.2729122,0.002032354],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7760063,0.08357716,0.1010463,0.0005373813,0.004315127,0.001472646,0.000135003,0.001193382,0.0317167],"genre_scores_gemma":[0.9895457,0.0004661611,0.007527521,0.0004101702,0.001393795,0.0001214306,0.000266073,0.0001884479,0.0000806819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4397771,"threshold_uncertainty_score":0.9996663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008461413484230108,"score_gpt":0.2499992180184147,"score_spread":0.2415378045341846,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}